Estimating the endpoint of a distribution in the presence of additive observation errors

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• 作者：Goldenshluger ; A. ; Tsybakov ; A.
• 关键词：62G07 ; 62G20 ; 65J20 ; Estimation of support of a probability density ; Deconvolution ; Extreme value distribution ; Optimal rates of convergence
• 刊名：Statistics and Probability Letters
• 出版年：2004
• 期刊代码：56_01677152
• 类别：mt
• 出版时间：June 1, 2004
• 卷：68
• 期：1
• 页码：39-49
• 文件大小：253 K

摘要

We consider the problem of estimating the endpoint of a probability distribution in the presence of observation errors, when the available sample is drawn from the convolution with some error density. We study the cases of Gaussian errors and errors with bounded support, and propose estimators that are optimal in a minimax sense.